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REF: implement _unbox to de-duplicate unwrapping #33906

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merged 2 commits into from
May 1, 2020
Merged

REF: implement _unbox to de-duplicate unwrapping #33906

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39e2df3
REF: implement _unbox to de-duplicate unwrapping
jbrockmendel Apr 30, 2020
a2f7183
update type annotation
jbrockmendel May 1, 2020
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65 changes: 27 additions & 38 deletions pandas/core/arrays/datetimelike.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
from pandas._libs import NaT, NaTType, Timestamp, algos, iNaT, lib
from pandas._libs.tslibs.c_timestamp import integer_op_not_supported
from pandas._libs.tslibs.period import DIFFERENT_FREQ, IncompatibleFrequency, Period
from pandas._libs.tslibs.timedeltas import Timedelta, delta_to_nanoseconds
from pandas._libs.tslibs.timedeltas import delta_to_nanoseconds
from pandas._libs.tslibs.timestamps import RoundTo, round_nsint64
from pandas._typing import DatetimeLikeScalar
from pandas.compat import set_function_name
Expand Down Expand Up @@ -52,6 +52,8 @@
from pandas.tseries import frequencies
from pandas.tseries.offsets import DateOffset, Tick

DTScalarOrNaT = Union[DatetimeLikeScalar, NaTType]


def _datetimelike_array_cmp(cls, op):
"""
Expand Down Expand Up @@ -122,12 +124,7 @@ def wrapper(self, other):
result = ops.comp_method_OBJECT_ARRAY(op, self.astype(object), other)
return result

if isinstance(other, self._scalar_type) or other is NaT:
other_i8 = self._unbox_scalar(other)
else:
# Then type(other) == type(self)
other_i8 = other.asi8

other_i8 = self._unbox(other)
result = op(self.asi8, other_i8)

o_mask = isna(other)
Expand Down Expand Up @@ -157,9 +154,7 @@ def _scalar_type(self) -> Type[DatetimeLikeScalar]:
"""
raise AbstractMethodError(self)

def _scalar_from_string(
self, value: str
) -> Union[Period, Timestamp, Timedelta, NaTType]:
def _scalar_from_string(self, value: str) -> DTScalarOrNaT:
"""
Construct a scalar type from a string.

Expand All @@ -179,13 +174,14 @@ def _scalar_from_string(
"""
raise AbstractMethodError(self)

def _unbox_scalar(self, value: Union[Period, Timestamp, Timedelta, NaTType]) -> int:
def _unbox_scalar(self, value: DTScalarOrNaT) -> int:
"""
Unbox the integer value of a scalar `value`.

Parameters
----------
value : Union[Period, Timestamp, Timedelta]
value : Period, Timestamp, Timedelta, or NaT
Depending on subclass.

Returns
-------
Expand All @@ -199,7 +195,7 @@ def _unbox_scalar(self, value: Union[Period, Timestamp, Timedelta, NaTType]) ->
raise AbstractMethodError(self)

def _check_compatible_with(
self, other: Union[Period, Timestamp, Timedelta, NaTType], setitem: bool = False
self, other: DTScalarOrNaT, setitem: bool = False
) -> None:
"""
Verify that `self` and `other` are compatible.
Expand Down Expand Up @@ -727,17 +723,16 @@ def _validate_fill_value(self, fill_value):
ValueError
"""
if is_valid_nat_for_dtype(fill_value, self.dtype):
fill_value = iNaT
fill_value = NaT
elif isinstance(fill_value, self._recognized_scalars):
self._check_compatible_with(fill_value)
fill_value = self._scalar_type(fill_value)
fill_value = self._unbox_scalar(fill_value)
else:
raise ValueError(
f"'fill_value' should be a {self._scalar_type}. "
f"Got '{str(fill_value)}'."
)
return fill_value

return self._unbox(fill_value)

def _validate_shift_value(self, fill_value):
# TODO(2.0): once this deprecation is enforced, use _validate_fill_value
Expand All @@ -764,8 +759,7 @@ def _validate_shift_value(self, fill_value):
)
fill_value = new_fill

fill_value = self._unbox_scalar(fill_value)
return fill_value
return self._unbox(fill_value)

def _validate_searchsorted_value(self, value):
if isinstance(value, str):
Expand Down Expand Up @@ -797,13 +791,7 @@ def _validate_searchsorted_value(self, value):
else:
raise TypeError(f"Unexpected type for 'value': {type(value)}")

if isinstance(value, type(self)):
self._check_compatible_with(value)
value = value.asi8
else:
value = self._unbox_scalar(value)

return value
return self._unbox(value)

def _validate_setitem_value(self, value):

Expand Down Expand Up @@ -836,19 +824,11 @@ def _validate_setitem_value(self, value):
raise TypeError(msg)

self._check_compatible_with(value, setitem=True)
if isinstance(value, type(self)):
value = value.asi8
else:
value = self._unbox_scalar(value)

return value
return self._unbox(value)

def _validate_insert_value(self, value):
if isinstance(value, self._recognized_scalars):
value = self._scalar_type(value)
self._check_compatible_with(value, setitem=True)
# TODO: if we dont have compat, should we raise or astype(object)?
# PeriodIndex does astype(object)
elif is_valid_nat_for_dtype(value, self.dtype):
# GH#18295
value = NaT
Expand All @@ -857,14 +837,16 @@ def _validate_insert_value(self, value):
f"cannot insert {type(self).__name__} with incompatible label"
)

self._check_compatible_with(value, setitem=True)
# TODO: if we dont have compat, should we raise or astype(object)?
# PeriodIndex does astype(object)
return value

def _validate_where_value(self, other):
if is_valid_nat_for_dtype(other, self.dtype):
other = NaT
elif isinstance(other, self._recognized_scalars):
other = self._scalar_type(other)
self._check_compatible_with(other, setitem=True)
elif not is_list_like(other):
raise TypeError(f"Where requires matching dtype, not {type(other)}")

Expand All @@ -881,13 +863,20 @@ def _validate_where_value(self, other):

if not type(self)._is_recognized_dtype(other.dtype):
raise TypeError(f"Where requires matching dtype, not {other.dtype}")
self._check_compatible_with(other, setitem=True)

self._check_compatible_with(other, setitem=True)
return self._unbox(other)

def _unbox(self, other) -> Union[np.int64, np.ndarray]:
"""
Unbox either a scalar with _unbox_scalar or an instance of our own type.
"""
if lib.is_scalar(other):
other = self._unbox_scalar(other)
else:
# same type as self
self._check_compatible_with(other)
other = other.view("i8")

return other

# ------------------------------------------------------------------
Expand Down
3 changes: 1 addition & 2 deletions pandas/core/arrays/period.py
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Original file line number Diff line number Diff line change
Expand Up @@ -249,8 +249,7 @@ def _unbox_scalar(self, value: Union[Period, NaTType]) -> int:
if value is NaT:
return value.value
elif isinstance(value, self._scalar_type):
if not isna(value):
self._check_compatible_with(value)
self._check_compatible_with(value)
return value.ordinal
else:
raise ValueError(f"'value' should be a Period. Got '{value}' instead.")
Expand Down